Development of a methodology for plausibility checks for linear structural mechanic finite element simulations using Deep Learning

使用深度学习开发线性结构力学有限元模拟的合理性检查方法

• 批准号: 456585803
• 负责人: Professor Dr.-Ing. Sandro Wartzack
• 金额: --
• 依托单位: Lehrstuhl für Konstruktionstechnik (KTmfk)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/456585803?language=en
• 关键词: Development; methodology; plausibility; checks; linear

In the current industrial environment, design accompanying linear finite element simulations are often carried out by product developers and not exclusively by calculation engineers with several years of professional experience. This leads to frequent iterations in the product development process and can lead to incorrect decisions based on insufficiently validated results. An automatic plausibility check for linear structural-mechanical FE simulations is an important method to support product developers. The use of Convolutional Neural Networks (CNN) and Machine Learning methods represents an enormous potential to identify correlations in data and to build a model with high prediction quality. In the applicant's preparatory work it could be shown that a plausibility check for FE calculations using Deep Learning and CNNs is possible. However, it is necessary to increase the prediction quality of the artificial neural network by adjusting the parameters and to demonstrate the application of the method to new simulations. Furthermore, local areas of the FE-simulation shall be investigated, especially to detect numerical errors like singularities.The aim of the project is to create a method for plausibility checks of similar linear structural-mechanical FE simulations based on the preliminary work on the projection method and singularity recognition. Furthermore, the network parameters of different CNNs and machine learning methods are to be optimized in order to implement a plausibility check with high prediction quality. FE-simulation results cannot be directly transferred to a neural network or machine learning algorithm, but have to be converted to a uniform computer-processable form. Within the framework of the research project, the projection method developed in preliminary work is applied, which uses spherical detector surfaces to transform arbitrary simulations into matrices with uniform size. The generated matrices contain all relevant information to classify a simulation as plausible or implausible. A Deep Learning CNN or SVM does the classification. In addition to the classification of the entire simulation, local areas should also be examined. Especially singularities in FE-simulations shall be detected and accordingly give feedback to the user.With an automatic plausibility check, errors in the simulation setup can be detected automatically at an early stage. It therefore represents an enormous potential for increasing the simulation quality in virtual product development. Especially if FE simulations are performed by product developers who have less simulation knowledge than experienced calculation engineers. A method will be developed which allows to consider similar geometries and simulation boundary conditions of linear FE-simulations.

在当前的工业环境中，伴随线性有限元模拟的设计通常由产品开发人员进行，而不仅仅是由具有多年专业经验的计算工程师进行。这会导致产品开发过程中的频繁迭代，并可能导致基于未经充分验证的结果的错误决策。线性结构力学有限元仿真的可扩展性自动检查是支持产品开发的重要方法。卷积神经网络（CNN）和机器学习方法的使用代表了识别数据相关性和构建高预测质量模型的巨大潜力。在申请人的准备工作中，可以证明使用深度学习和CNN对FE计算进行可验证性检查是可能的。然而，有必要通过调整参数来提高人工神经网络的预测质量，并证明该方法在新模拟中的应用。此外，还应研究有限元模拟的局部区域，特别是检测数值误差，如奇异性。本项目的目的是在投影法和奇异性识别的基础上，创建一种类似线性结构力学有限元模拟的可验证性检查方法。此外，不同CNN和机器学习方法的网络参数将被优化，以实现具有高预测质量的可扩展性检查。FE仿真结果不能直接传输到神经网络或机器学习算法，而是必须转换为统一的计算机可处理形式。在该研究项目的框架内，应用了在前期工作中开发的投影方法，该方法使用球形探测器表面将任意模拟转换为具有统一大小的矩阵。生成的矩阵包含所有相关信息，以将模拟分类为合理或不合理。深度学习CNN或SVM进行分类。除了对整个模拟进行分类外，还应检查局部区域。特别是FE模拟中的奇异点应被检测并相应地向用户提供反馈。通过自动可扩展性检查，可以在早期阶段自动检测模拟设置中的错误。因此，它代表了一个巨大的潜力，提高虚拟产品开发的仿真质量。特别是如果FE模拟是由模拟知识少于经验丰富的计算工程师的产品开发人员执行的。将开发一种方法，允许考虑类似的几何形状和模拟边界条件的线性有限元模拟。